Knee Point Detection for Cloud Application Bottleneck Identification
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Solution Overview
Problem
Detecting and pinpointing bottleneck causes in cloud-based multi-tier applications is challenging due to complex resource usage and performance noise, leading to false positives and negatives, which complicates system diagnosis and resource optimization.
Innovation Solution
A method and system that monitor throughput of resources within a selected time window to identify a 'knee point' in resource usage curves, representing the bottleneck, by detecting change rates and correlating temporal occurrences across tiers to determine the initial bottleneck cause.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional bottleneck detection methods are used to monitor system resources, then comprehensive resource monitoring is achieved, but false positives and false negatives increase due to performance noise
Solution Approach 1:
The patent segments the bottleneck detection process into multiple stages: (1) collecting resource usage data from multiple tiers, (2) identifying knee points in throughput curves for each resource, (3) temporal correlation of knee points across tiers, and (4) ranking resources to identify the root cause. This segmentation allows the system to filter out noise at each stage rather than being affected by it throughout the entire process, thereby improving both reliability and measurement precision.
Solution Approach 2:
The patent performs preliminary actions by first identifying knee points in throughput curves before attempting to correlate resources. By pre-processing the data to extract meaningful knee point information and establishing temporal relationships in advance, the system reduces the impact of performance noise during the actual bottleneck identification process, leading to more accurate results.
2Loss of information
If manual bottleneck analysis is performed across distributed resources, then detailed system diagnosis is achieved, but time consumption and operational burden increase significantly
Solution Approach 1:
The patent implements self-service by enabling the system to automatically collect resource usage data, identify knee points, perform temporal correlation analysis, and rank resources to determine the root cause bottleneck. This automated self-diagnosis capability eliminates the need for manual analysis while maintaining complete system diagnosis information, significantly reducing both time consumption and operational burden.
Solution Approach 2:
The system continuously monitors resource usage and provides feedback through automatic bottleneck identification and ranking. This feedback mechanism allows the system to self-correct and self-optimize by automatically adjusting to changing system conditions without manual intervention, maintaining information completeness while minimizing time loss.
3Adaptability or versatility
If resource usage data is collected from all tiers and servers, then comprehensive bottleneck analysis is enabled, but data complexity and processing difficulty increase
Solution Approach 1:
The patent extracts only the essential information needed for bottleneck identification by focusing on knee points in throughput curves rather than processing all raw resource usage data. This extraction approach maintains comprehensive multi-tier monitoring capability while significantly reducing data processing complexity by filtering out unnecessary information and concentrating on the most relevant metrics.
Data Source
AI summary
Methods and systems are provided for determining performance characteristics of an application processing system. The method comprises monitoring throughput of a plurality of resources of the system in a selected time window. A change rate is detected in the throughput of the resources, respectively, representative of a change to constancy of workload in at least some of the resources. Such a change in constancy comprises a knee point of a plot of resource usage comprising load relative to throughput. The time of the change rate is identified within the time window. A relatively first to occur of a plurality of resources knee points is determined wherein the resource corresponding to the first to occur is determined to have a fully loaded throughput within the multi-tier processing system. The determination of the first to occur knee point comprises pinpointing a bottleneck starting point within the application processing system.